GPT-6.1 Terra vs Gemini 4 Argon: One Is Cheaper, One Is Right More Often

On the DualEntry accounting benchmark published October 26, 2026, Gemini 4 Argon scored 91.1% and GPT-6.1 Terra scored 78.2% — and Terra did it in 16.1 seconds for 4.2 cents, the fastest and cheapest run of any OpenAI model in that 22-model set. That one row is the matchup in miniature. Google's flagship is the better judgment engine; OpenAI's new mid-tier is the better throughput engine; and the gap between those two jobs is where the money actually goes. Choosing between them on the sticker price alone gets the answer wrong in both directions.

The specs, side by side, because the details decide more than the headline numbers:

• Price — GPT-6.1 Terra $1.00 per 1M input and $5.00 per 1M output, rising to $2.00/$7.50 once a request exceeds 272K input tokens; Gemini 4 Argon $2.00/$10.00 introductory through November 30, 2026, then $4.00/$20.00.

• Context — Terra 1.05M tokens with a 128K output cap; Argon roughly 1M input (Google has not published a figure; trackers list 1M) and up to 1M tokens in a single output through its Long Decode Continuation feature.

• Inputs — Terra accepts text and images; Artificial Analysis lists the same two modalities for Argon, while Google's announcement and its long-video benchmarks indicate video input as well.

• Reasoning control — Terra exposes six effort levels; Argon exposes three.

• Knowledge cutoff — Terra June 2026; Argon March 2026.

• Independent Intelligence Index — Terra 48, Argon 53, on Artificial Analysis's current scale.

The price gap everyone quotes is the wrong one

The launch coverage frames this as a two-times price difference: $1/$5 against $2/$10. That is true only during Argon's introductory window, and only per token. After November 30 the output price quadruples relative to Terra, not doubles. And per token is not how anyone pays for agent work.

Artificial Analysis puts Terra at about $0.40 per Intelligence Index task and Argon at $1.62 — a fourfold gap, not a twofold one. The cause is mostly the rate card rather than runaway verbosity: on that same evaluation Argon generated 110M output tokens against Terra's 130M, so Terra was actually the chattier of the two and still finished at a quarter of the cost. What Terra buys is a cheaper token and a faster answer, not a shorter one.

There is a second cliff that launch coverage mostly misses. Terra's $1.00 input price applies for the first 272K tokens of a request. Past that, input jumps to $2.00 and output to $7.50. For chat and code review this never matters. For a 600K-token contract review or a long-horizon repository sweep, Terra's effective price doubles mid-request and the gap against Argon narrows sharply. Budget for the request size you actually send, not the one in the pricing table's first row.

Where Argon's lead is real, not marketing

Argon's advantage is concentrated in work where a wrong answer costs more than the tokens to prevent it. The DualEntry result is the cleanest public example: 91.1% overall against Terra's 78.2%, with the widest single gap in accounts payable, where Argon scored 84.6% and Terra 53.8%. Terra finished tied for 18th of 22 models on that benchmark. It was also the fastest, which tells you what kind of model it is: optimized to clear a queue, not to be the last line of defense.

The same pattern shows up elsewhere. On the Vals Index, Argon sits at the top of the field at 68.9%, first of roughly 41 models. On Artificial Analysis's Intelligence Index it scores 53 against Terra's 48. On AA-Omniscience, its hallucination rate is around 15% — the lowest measured among frontier models — while GPT-6 Astra, the model it is most often compared with, sits near 51%. Terra's own guess rate fell from 88% to 38% versus its predecessor, which is a genuine improvement, but those two figures are not the same metric and should not be subtracted from each other. The honest reading is that Argon is the more cautious model and Terra is the much-improved but still more speculative one.

Argon's input surface is wider on one axis: video. Artificial Analysis's technical panel lists text and image only, but Google's announcement frames expanded multimodal capability and benchmarks long-video understanding on LVBench, which only makes sense if video goes in. If your pipeline ingests recorded calls or screen captures, verify the exact formats against Google's documentation before building an integration around them — where they hold, it is a capability difference that no Terra price advantage closes.

Where Terra is the rational default

Terra's case is not that it is smarter. It is that most production traffic does not need the smartest model, and Terra is unusually good at being good enough cheaply.

It is fast in the way that changes system design: 90.9 output tokens per second on Artificial Analysis's measurement against Argon's 80.9, with OpenAI claiming roughly three times faster task completion than its own flagship. A support triage queue, a log summarizer, a first-pass code reviewer, or a document classifier running a million times a month does not benefit from Argon's judgment margin. It benefits from a model that returns in seconds at $0.40 a task instead of dollars a task.

Terra is also the more controllable of the two. Six reasoning levels against Argon's three means finer granularity in the one dial that most affects both cost and latency. Teams that route easy requests to low effort and hard ones to max effort can often land within a few points of a bigger model's accuracy at a fraction of its bill. Argon gives you less room to make that trade.

And Terra's newer knowledge cutoff — June 2026 against March 2026 — matters for anything that reasons about recent events, library versions, or pricing that changed this summer. It is a small edge, but it is free.

The decision rule

Do not pick one. The evidence supports a split, and the split is unusually clean:

• Use GPT-6.1 Terra for high-volume, short-output, supervised work where a wrong answer is caught downstream: classification, extraction, routing, first-draft generation, log and alert triage, bulk code review.

• Use Gemini 4 Argon for low-volume, long-horizon, high-cost-of-error work: financial and accounting reconciliation, legal and compliance review, multi-step agents operating tools unsupervised, and anything that needs video, audio, or PDF input.

• Use Terra as the first pass and Argon as the escalation for the tail. The 78.2-versus-91.1 gap on accounting work is exactly the kind of margin where a cheap first pass plus an expensive check beats either model alone.

• Re-run the arithmetic after November 30. Argon's move from $2/$10 to $4/$20 changes the crossover point substantially, and any comparison written during the promotional window will be stale.

How to run either one

Availability differs more than the benchmarks suggest. Terra shipped October 26 through OpenAI's API, ChatGPT, and Codex, with no open weights. Argon has been rolling out in phases since its September 30 announcement — first to vetted cyber defenders through Google's Fairwind program, then to AI Ultra subscribers and paid API customers — and Google has not committed to a broad free release.

As of October 27, neither model is on OrcaRouter's catalogue, so this is not a case where we can hand you a single endpoint for both. What we can tell you is how the surrounding stack usually gets built: teams keep a cheap executor and an expensive checker behind one API, and the models that are already routed — Claude Opus 5.5, GPT-6.1 Sol, Grok 4.7, and more than 200 others — sit behind a single key with provider list pricing passed through at 0% markup and automatic failover when a provider degrades. If Terra or Argon lands in the catalogue, it slots into the same key rather than becoming a second integration. Until then, test each through its vendor and keep the routing decision at the task level, where it belongs.

The thing to watch is not another benchmark chart. It is Argon's post-promotional price and Terra's first independent Terminal-Bench run, which currently rests on OpenAI's own 50.3% claim against Argon's 57%-plus. If Terra holds near that number at a quarter of Argon's per-task cost, the split above becomes the default architecture for a lot of teams. If it drifts, the cheap-first-pass strategy gets more expensive than it looks.